Evidence map›Paper›PMID 37633934›Full record

ArticleNature communications2023

Universal patterns in egocentric communication networks.

Gerardo Iñiguez, Sara Heydari, János Kertész, Jari Saramäki

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gerardo IñiguezDepartment of Network and Data Science, Central European University, 1100, Vienna, Austria. iniguezg@ceu.edu.ORCID http://orcid.org/0000-0001-7181-5520
Sara HeydariDepartment of Computer Science, Aalto University School of Science, 00076, Aalto, Finland.ORCID http://orcid.org/0000-0001-5788-6162
János KertészDepartment of Network and Data Science, Central European University, 1100, Vienna, Austria.ORCID http://orcid.org/0000-0003-4957-5406
Jari SaramäkiDepartment of Computer Science, Aalto University School of Science, 00076, Aalto, Finland. jari.saramaki@aalto.fi.ORCID http://orcid.org/0000-0002-5904-4062

Funding

EC | CHIST-ERA FWF I 5205-NEC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 952026United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research) FA8655-20-1-7020
6 · The paper itself

Abstract

Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at the network and individual levels. Egocentric networks, networks of relationships around an individual, exhibit few strong ties and more weaker ties, as evidenced by electronic communication records. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and driving mechanisms of social tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets of interactions between millions of people during months to years. We find universality in tie strength distributions and their individual-level variation across communication modes, even in channels not reflecting offline social relationships. Via a simple model of egocentric network evolution, we show that the observed universality arises from the competition between cumulative advantage and random choice, two tie reinforcement mechanisms whose balance determines the diversity of tie strengths. Our results provide insight into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.

Identifiers

PMID37633934
PMCPMC10460427

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.